Installation Options for Kubeflow Pipelines

Overview of the ways to deploy Kubeflow Pipelines

Kubeflow Pipelines offers a few installation options.This page describes the options and the features availablewith each option:

• A standalone Kubeflow Pipelines deployment.
• Kubeflow Pipelines as part of a full Kubeflow deployment.
• Alpha: GCP Hosted ML Pipelines.

Kubeflow Pipelines Standalone

Use this option to deploy Kubeflow Pipelines to an on-premises or cloudKubernetes cluster, without the other components of Kubeflow.To deploy Kubeflow Pipelines Standalone, you use kustomize manifests only.This process makes it simpler to customize your deployment and to integrateKubeflow Pipelines into an existing Kubernetes cluster.

• Installation guide
• Kubeflow Pipelines Standalone deploymentguide
• Interfaces
• • Kubeflow Pipelines UI
  • Kubeflow Pipelines SDK
  • Kubeflow Pipelines API
• Notes on specific features
• After deployment, your Kubernetes cluster contains Kubeflow Pipelines only.It does not include the other Kubeflow components.For example, to use a Jupyter Notebook, you must use a local notebook or ahosted notebook in a cloud service such as the AI PlatformNotebooks.

Full Kubeflow deployment

Use this option to deploy Kubeflow Pipelines to your local machine, on-premises,or to a cloud, as part of a full Kubeflow installation.

• Installation guide
• Kubeflow installation guide
• Interfaces:
  • Kubeflow UI
  • Kubeflow Pipelines UI within or outside the Kubeflow UI
  • Kubeflow Pipelines SDK
  • Kubeflow Pipelines API
  • Other Kubeflow APIs
• Notes on specific features
• After deployment, your Kubernetes cluster includes all theKubeflow components.For example, you can use the Jupyter notebook servicesdeployed with Kubeflow to create one or more notebookservers in your Kubeflow cluster.

GCP Hosted ML Pipelines

Alpha release

GCP Hosted ML Pipelines is currently in Alpha with limited support. The Kubeflow team is interested in any feedback you may have, in particular on the usability of the feature. To get access to the Alpha release, email kfp-mkp-alpha-feedback@googlegroups.com. You can raise any issues or discussion items in the Kubeflow Pipelines issue tracker.

Use this option to deploy Kubeflow Pipelines to Google Kubernetes Engine (GKE)from GCP Marketplace. You can deploy Kubeflow Pipelines to an existing or newGKE cluster and manage your cluster within GCP.

• Installation guide
• Deploy Kubeflow Pipelines from Google CloudMarketplace
• Interfaces
• • GCP Console for managing the Kubeflow Pipelines cluster and other GCPservices.
  • Kubeflow Pipelines UI via the Open Pipelines Dashboard link in theGCP Console
  • Kubeflow Pipelines SDK in Cloud Notebooks
• Notes on specific features
• After deployment, your Kubernetes cluster contains Kubeflow Pipelines only.It does not include the other Kubeflow components.For example, to use a Jupyter Notebook, you can use AI PlatformNotebooks.